GeoPandas

import geopandas as gpd

import pandas as pd

gdf = gpd.read_parquet("~/data/scag_region.parquet")

gdf.shape

(4580, 194)

type(gdf)

geopandas.geodataframe.GeoDataFrame

gdf.head()

	geoid	n_asian_under_15	n_black_under_15	n_hispanic_under_15	n_native_under_15	n_white_under_15	n_persons_under_18	n_asian_over_60	n_black_over_60	n_hispanic_over_60	...	year	n_total_housing_units_sample	p_nonhisp_white_persons	p_white_over_60	p_black_over_60	p_hispanic_over_60	p_native_over_60	p_asian_over_60	p_disabled	geometry
0	06037128702	58.0	0.0	223.0	0.0	475.0	986.0	NaN	NaN	NaN	...	2010	2903.0	64.726214	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.44870 34.16485, -118.43997 34.1...
1	06037131600	83.0	62.0	777.0	0.0	135.0	1355.0	NaN	NaN	NaN	...	2010	1487.0	28.679979	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.56229 34.22033, -118.55792 34.2...
2	06037134104	287.0	17.0	816.0	0.0	61.0	1323.0	NaN	NaN	NaN	...	2010	1388.0	14.846188	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.57976 34.21558, -118.57539 34.2...
3	06037134304	90.0	24.0	298.0	0.0	89.0	520.0	NaN	NaN	NaN	...	2010	928.0	33.378933	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.61472 34.21952, -118.61039 34.2...
4	06037242000	0.0	229.0	681.0	0.0	0.0	1164.0	NaN	NaN	NaN	...	2010	1054.0	0.058565	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.25416 33.93882, -118.25413 33.9...

5 rows × 194 columns

gdf.columns.values

array(['geoid', 'n_asian_under_15', 'n_black_under_15',
       'n_hispanic_under_15', 'n_native_under_15', 'n_white_under_15',
       'n_persons_under_18', 'n_asian_over_60', 'n_black_over_60',
       'n_hispanic_over_60', 'n_native_over_60', 'n_persons_over_60',
       'n_white_over_60', 'n_asian_over_65', 'n_black_over_65',
       'n_hispanic_over_65', 'n_native_over_65', 'n_white_over_65',
       'n_persons_over_75', 'n_persons_over_15', 'n_civilians_over_16',
       'n_civilians_over_18', 'n_persons_over_25', 'n_age_5_older',
       'n_asian_age_distribution', 'n_black_age_distribution',
       'n_hispanic_age_distribution', 'n_native_age_distribution',
       'n_white_age_distribution', 'n_asian_persons', 'n_black_persons',
       'n_chinese_persons', 'n_labor_force', 'n_civilians_16_64',
       'n_edu_college_greater', 'n_cuban_pop',
       'n_poverty_determined_asian', 'n_poverty_determined_black',
       'n_total_pop_sample', 'n_female_over_16',
       'n_poverty_determined_families', 'n_poverty_determined_hispanic',
       'n_disabled', 'n_housing_units_multiunit_structures_denom',
       'n_poverty_determined_native', 'n_poverty_determined_persons',
       'n_poverty_determined_white', 'n_employed_over_16',
       'n_total_families', 'n_foreign_born_pop',
       'n_female_headed_families', 'n_filipino_persons',
       'n_female_labor_force', 'n_german_pop', 'n_german_born_pop',
       'n_household_recent_move', 'n_structures_30_old',
       'n_hawaiian_persons', 'n_total_households', 'n_asian_households',
       'n_black_households', 'n_hispanic_households',
       'n_white_households', 'median_household_income',
       'median_income_asianhh', 'median_income_blackhh',
       'median_income_hispanichh', 'median_income_whitehh',
       'n_hispanic_persons', 'n_edu_hs_less', 'n_total_housing_units',
       'per_capita_income', 'n_asian_indian_persons', 'n_irish_pop',
       'n_irish_born_pop', 'n_italian_pop', 'n_italian_born_pop',
       'n_japanese_persons', 'n_korean_persons', 'n_limited_english',
       'n_employed_manufacturing', 'n_married', 'n_mexican_pop',
       'median_home_value', 'median_contract_rent',
       'n_housing_units_multiunit_structures', 'n_recent_immigrant_pop',
       'n_poverty_over_65', 'n_poverty_asian', 'n_naturalized_pop',
       'n_poverty_black', 'n_poverty_families_children',
       'n_nonhisp_black_persons', 'n_poverty_hispanic',
       'n_nonhisp_white_persons', 'n_poverty_native', 'n_poverty_persons',
       'n_native_persons', 'n_poverty_white', 'n_occupied_housing_units',
       'n_other_language', 'n_owner_occupied_housing_units',
       'p_recent_immigrant_pop', 'p_household_recent_move',
       'p_asian_under_15', 'p_black_under_15', 'p_hispanic_under_15',
       'p_native_under_15', 'p_white_under_15', 'p_persons_under_18',
       'p_structures_30_old', 'p_persons_over_60', 'p_asian_over_65',
       'p_black_over_65', 'p_hispanic_over_65', 'p_native_over_65',
       'p_poverty_rate_over_65', 'p_white_over_65', 'p_persons_over_75',
       'p_poverty_rate_asian', 'p_asian_persons', 'p_poverty_rate_black',
       'p_chinese_persons', 'p_edu_college_greater', 'p_cuban_pop',
       'p_foreign_born_pop', 'p_female_headed_families',
       'p_filipino_persons', 'p_female_labor_force',
       'p_poverty_rate_children', 'p_german_pop', 'p_german_born_pop',
       'p_hawaiian_persons', 'p_hispanic_persons',
       'p_poverty_rate_hispanic', 'p_edu_hs_less',
       'p_asian_indian_persons', 'p_irish_pop', 'p_irish_born_pop',
       'p_italian_pop', 'p_italian_born_pop', 'p_japanese_persons',
       'p_korean_persons', 'p_limited_english',
       'p_employed_manufacturing', 'p_married', 'p_mexican_pop',
       'p_housing_units_multiunit_structures', 'p_poverty_rate_native',
       'p_naturalized_pop', 'p_nonhisp_black_persons', 'p_black_persons',
       'p_native_persons', 'p_other_language', 'n_total_pop',
       'p_owner_occupied_units', 'p_poverty_rate', 'p_puerto_rican_pop',
       'p_employed_professional', 'n_puerto_rican_pop',
       'n_employed_professional', 'p_russian_pop', 'p_russian_born_pop',
       'p_scandanavian_pop', 'p_scandanavian_born_pop',
       'p_employed_self_employed', 'p_unemployment_rate',
       'p_vacant_housing_units', 'p_veterans', 'p_vietnamese_persons',
       'p_widowed_divorced', 'p_poverty_rate_white',
       'n_renter_occupied_housing_units', 'n_russian_pop',
       'n_russian_born_pop', 'n_scandaniavian_pop',
       'n_scandaniavian__born_pop', 'n_employed_self_employed',
       'n_unemployed_persons', 'n_vacant_housing_units', 'n_veterans',
       'n_vietnamese_persons', 'n_widowed_divorced', 'n_white_persons',
       'year', 'n_total_housing_units_sample', 'p_nonhisp_white_persons',
       'p_white_over_60', 'p_black_over_60', 'p_hispanic_over_60',
       'p_native_over_60', 'p_asian_over_60', 'p_disabled', 'geometry'],
      dtype=object)

gdf.n_total_pop

0       5497.0
1       5659.0
2       4486.0
3       2924.0
4       3415.0
         ...  
4575    3672.0
4576    5257.0
4577    6765.0
4578    2981.0
4579    3994.0
Name: n_total_pop, Length: 4580, dtype: float64

gdf.geometry

0       POLYGON ((-118.44870 34.16485, -118.43997 34.1...
1       POLYGON ((-118.56229 34.22033, -118.55792 34.2...
2       POLYGON ((-118.57976 34.21558, -118.57539 34.2...
3       POLYGON ((-118.61472 34.21952, -118.61039 34.2...
4       POLYGON ((-118.25416 33.93882, -118.25413 33.9...
                              ...                        
4575    POLYGON ((-118.50373 34.42607, -118.50050 34.4...
4576    POLYGON ((-118.20731 33.90754, -118.20641 33.9...
4577    POLYGON ((-119.22134 34.18130, -119.21727 34.1...
4578    POLYGON ((-116.51068 33.80502, -116.51069 33.8...
4579    POLYGON ((-118.41379 34.17940, -118.41160 34.1...
Name: geometry, Length: 4580, dtype: geometry

gdf.plot()

<Axes: >

Projections

gdf.crs

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

gdf1 = gdf.to_crs(3857)

gdf1.crs

<Projected CRS: EPSG:3857>
Name: WGS 84 / Pseudo-Mercator
Axis Info [cartesian]:
- X[east]: Easting (metre)
- Y[north]: Northing (metre)
Area of Use:
- name: World between 85.06°S and 85.06°N.
- bounds: (-180.0, -85.06, 180.0, 85.06)
Coordinate Operation:
- name: Popular Visualisation Pseudo-Mercator
- method: Popular Visualisation Pseudo Mercator
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

gdf1.plot()

<Axes: >

gdf.median_home_value

0       647272.659176
1       400842.977528
2       416741.666667
3       406178.838951
4       251438.857678
            ...      
4575    291838.951311
4576    273871.254682
4577    293254.588015
4578    255794.662921
4579    581717.790262
Name: median_home_value, Length: 4580, dtype: float64

gdf.plot(column='median_home_value')

<Axes: >

gdf.plot(column='median_home_value', legend=True)

<Axes: >

gdf.plot(column='median_home_value', legend=True,
        scheme='quantiles', k=10)

<Axes: >

gdf.plot(column='p_hispanic_persons', legend=True,
        scheme='quantiles', k=10)

<Axes: >

gdf.explore(column='p_hispanic_persons', tooltip=['geoid', 'p_hispanic_persons'])

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